Apparatus and methods for computerized authentication of electronic documents

ABSTRACT

A computerized method for authenticating documents having VIZ sections, the method comprising capturing an image of a document to be authenticated from a scanner and enhancing the captured image, and using a processor for identifying and cropping a VIZ section in the image.

REFERENCE TO CO-PENDING APPLICATIONS

This is a Continuation of application Ser. No. 16/552,308, filed Aug.27, 2019, which is a Continuation of application Ser. No. 15/467,540filed Mar. 23, 2017, now U.S. Pat. No. 10,440,219, which is aContinuation of application Ser. No. 14/734,398 filed Jun. 9, 2015, nowU.S. Pat. No. 9,628,661, which is a Continuation of application Ser. No.13/509,169 filed May 10, 2012, now U.S. Pat. No. 9,081,988, which is anational stage entry of PCT/IL2010/000932 filed Nov. 10, 2010. Priorityis claimed from Israel patent application No. 20208 entitled Apparatusand Methods for Computerized Authentication of Electronic Documents andfiled 10 Nov. 2009.

FIELD OF THE INVENTION

The present invention relates generally to computerized analysis ofdocuments and more particularly to computerized fraud detection.

BACKGROUND OF THE INVENTION

The state of the art includes the following publications:

U.S. Pat. No. 6,621,916 B1 (SMITH et al.) GB 2059129 A (SODECO) GB2454821 A (CANADIAN BANK NOTE) EP 1473657 A1 (SICPA HOLDING) EP 0981806A1 (CUMMINS-ALLISON) U.S. Pat. No. 5,729,623 A (OMATU et al.)

The disclosures of all publications and patent documents mentioned inthe specification, including those listed in the reference to co-pendingapplications, and of the publications and patent documents cited in thespecification directly or indirectly, are hereby incorporated byreference.

SUMMARY OF THE INVENTION

Certain embodiments of the present invention seek to provide improvedapparatus and methods for computerized analysis of documents.

Certain embodiments of the present invention seek to provide improvedapparatus and methods for computerized fraud detection.

There is thus provided, in accordance with at least one embodiment ofthe present invention, a computerized method for authenticatingdocuments having VIZ sections, the method comprising capturing an imageof a document to be authenticated from a scanner and enhancing thecaptured image; and identifying and cropping a VIZ section in the image.

Further in accordance with at least one embodiment of the presentinvention, the method also comprises binarization for optimizing OCRreadability; definition of fields for OCR operation; and identificationand reading of at least one heading of at least one of the fields.

Still further in accordance with at least one embodiment of the presentinvention, the method also comprises optimization of OCR according totemplates.

Additionally in accordance with at least one embodiment of the presentinvention, the method also comprises at least one of final informationidentification, error correction and output control.

Also provided, in accordance with at least one embodiment of the presentinvention, is a computerized document processing method for analyzingelectronic documents, the method comprising analyzing a binarycharacteristic, having two possible values, for each electronicdocument; and selecting one of the two possible values for at least someof the electronic documents.

In contrast, conventional systems generate a summary for each electronicdocument representing an analysis thereof vis a vis the binarycharacteristic, and do not utilize the analysis to come to a decisionregarding the correct value for the binary characteristic of anyindividual document.

Further in accordance with at least one embodiment of the presentinvention, the binary characteristic comprises an authenticitycharacteristic and wherein the two possible values represent anindication that a document is authentic and forged, respectively.

Still further in accordance with at least one embodiment of the presentinvention, the binary characteristic comprises a document compliancecharacteristic and the two possible values represent an indication thata document bearer is compliant with regulations and non-compliant withregulations, respectively.

Additionally in accordance with at least one embodiment of the presentinvention, the method also comprises generating an output other than thetwo possible values for at least some of the electronic documents.

Further in accordance with at least one embodiment of the presentinvention, the output comprises a conditional ok.

Also provided, in accordance with at least one embodiment of the presentinvention, is a computerized document processing system for analyzingincoming electronic documents, the system comprising a first sub-systemfor checking at least one of the following for each of the incomingdocuments: integrity of document materials; integrity of documentmarkings; and consistency of data within document; a database ofdocuments; and a document-database consistency analyzer operative toascertain consistency of incoming documents vis a vis the database.

Also provided, in accordance with at least one embodiment of the presentinvention, is a computerized document processing system for analyzingincoming electronic documents, the system comprising a first sub-systemfor checking at least one of the following for each of the incomingdocuments: integrity of document materials; integrity of documentmarkings; and consistency of data within document.

Also provided, in accordance with at least one embodiment of the presentinvention, is a computerized document processing system for analyzingauthenticity of incoming electronic documents, the system comprising aworking day database storing information indicating dates which are notworking days; an issue date finder operative to find an issue datewithin an electronic document; and an issue date checker operative togenerate an indication as to whether the issue date found by the finderis indicated by the working day database to be a workday.

Further in accordance with at least one embodiment of the presentinvention, the working day database includes per-country information,the system also comprising a country identifier operative to identify acountry which issued an individual incoming electronic document andwherein the issue date checker uses per-country information whichcorresponds to the country as identified by the country identifier.

Also provided, in accordance with at least one embodiment of the presentinvention, is a computerized document processing method for establishingidentity authentication for incoming electronic documents, the methodcomprising determining the authenticity of a document based onparameters extracted from the document, including providing a pluralityof parameters characterizing the document and comparing each individualparameter from among the plurality of parameters to a correspondingplurality of known values thereby to generate a corresponding pluralityof comparison results; assigning a plurality of weights to the pluralityof comparison results respectively, at least one individual weight fromamong the plurality of weights being based on the weight's correspondingparameter's cumulative success at distinguishing authentic documentsfrom non-authentic documents; and generating an authenticitydetermination by computing a weighted combination of the plurality ofcomparison results using the plurality of weights.

Further in accordance with at least one embodiment of the presentinvention, the parameters represent at least one of visualcharacteristics, content characteristics and physical characteristics ofthe document.

Still further in accordance with at least one embodiment of the presentinvention, the providing a plurality of parameters includes at least oneof the following: computing at least one parameter internally;extracting at least one parameter from the document; receiving at leastone parameter computed in an external system; and receiving at least onemanually entered parameter.

Also provided, in accordance with at least one embodiment of the presentinvention, is a computerized document processing system for analyzingincoming multi-level electronic documents, the system comprising abinarization functionality operative to generate binarizedrepresentations of incoming multi-level electronic documents by applyinga set of at least one binarization thresholds to the multi-levelelectronic documents; and a learning subsystem operative to accumulateexperience and to dynamically change the binarization thresholds basedon the experience.

Further in accordance with at least one embodiment of the presentinvention, the system also comprises a document analyzer operative toprocess the binarized representations in order to generate documentanalysis results and wherein the learning subsystem conducts anevaluation of the document analysis results as a function of thebinarization thresholds and dynamically changes the thresholds based onthe evaluation.

Still further in accordance with at least one embodiment of the presentinvention, the document analysis results include indicationsdistinguishing known authentic documents from known non-authenticdocuments.

Also provided, in accordance with at least one embodiment of the presentinvention, is a computerized document processing method for analyzingincoming multi-level electronic documents, the method comprisinganalyzing the incoming documents including binarizing the documentsusing a set of binarization thresholds and computing a weightedcombination of parameters characterizing the document, the weightedcombination defining a set of weights; and at least one of the sets isat least partly determined dynamically as a function of the system'schanging state of knowledge including knowledge regarding changes intolerances of processes used to produce the documents.

Also provided, in accordance with at least one embodiment of the presentinvention, is a computerized document processing system for analyzingincoming electronic documents, the system comprising a database storinga plurality of templates each corresponding to an individual series ofan individual type of document in an individual country; and apparatusfor maintaining the database including a matcher operative to identifyincoming documents which do not match any of the plurality of templates,to generate a new template in the database, each time an incomingdocument is found not to match any of the plurality of templates and,for each individual template from among the plurality of templates, tostatistically analyze those incoming documents which match theindividual template and to update the individual template accordingly.

Further in accordance with at least one embodiment of the presentinvention, the matcher identifies incoming documents which do not matchany of the plurality of templates by using initial tolerance values toidentify a population of incoming documents matching an individualtemplate, statistically analyzing that population of incoming documentswhich matches the individual template including estimating the variationof that population for at least one document parameter, and modifyingthe initial tolerance values to reflect the variation.

Yet further provided, in accordance with at least one embodiment of thepresent invention, is a computerized document processing system foranalyzing incoming electronic documents, having VIZ and MRZ zones, thesystem comprising apparatus for comparing data in the VIZ with data inthe MRZ and to evaluate consistency accordingly.

Also provided, in accordance with at least one embodiment of the presentinvention, is a computerized document processing system for analyzingincoming electronic documents, having VIZ and MRZ zones, the systemcomprising apparatus for supplying images of an incoming document in aplurality of scanned illuminations as well as a photo image from visibleillumination, wherein each image is supplied immediately after a certainillumination has been scanned and even before all illuminations arecompleted.

Further in accordance with at least one embodiment of the presentinvention, the system also comprises document recognition apparatusgenerating document recognition results generated step by step as thescanned illuminations become available, thereby to define a sequence ofpartial results, and the recognition apparatus is operative to supplythe partial results before the document has been completely scanned.

Still further in accordance with at least one embodiment of the presentinvention, when complete document recognition results have beengenerated, a special event is fired so as to enable a host applicationto access the complete results.

Also provided, in accordance with at least one embodiment of the presentinvention, is a method for identifying fraudulent documents, the methodincluding providing a scanned document; and analyzing the scanneddocument in order to determine whether dithering is present in thescanned document.

Further in accordance with at least one embodiment of the presentinvention, analyzing comprises computing a maximum dispersion of colorvalues of pixels in a selected area of the scanned document, andcomparing the maximum dispersion to an expected value therefor.

Still further in accordance with at least one embodiment of the presentinvention, the method also comprises analyzing the captured image inorder to determine whether dithering is present.

Additionally in accordance with at least one embodiment of the presentinvention, the system comprises a working day database storinginformation indicating dates which are not working days; an issue datefinder operative to find an issue date within an electronic document;and an issue date checker operative to generate an indication as towhether the issue date found by the finder is indicated by the workingday database to be a workday.

Further in accordance with at least one embodiment of the presentinvention, the analyzing and selecting comprises determining theauthenticity of a document based on parameters extracted from thedocument, including providing a plurality of parameters characterizingthe document and comparing each individual parameter from among theplurality of parameters to a corresponding plurality of known valuesthereby to generate a corresponding plurality of comparison results;assigning a plurality of weights to the plurality of comparison resultsrespectively, at least one individual weight from among the plurality ofweights being based on the weight's corresponding parameter's cumulativesuccess at distinguishing authentic documents from non-authenticdocuments; and generating an authenticity determination by computing aweighted combination of the plurality of comparison results using theplurality of weights.

Further in accordance with at least one embodiment of the presentinvention, the analyzing and selecting comprises analyzing the incomingdocuments including binarizing the documents using a set of binarizationthresholds and computing a weighted combination of parameterscharacterizing the document, the weighted combination defining a set ofweights; and at least one of the sets is at least partly determineddynamically as a function of the system's changing state of knowledgeincluding knowledge regarding changes in tolerances of processes used toproduce the documents.

Still further in accordance with at least one embodiment of the presentinvention, incoming electronic documents have VIZ and MRZ zones and thesystem also comprises apparatus for comparing data in the VIZ with datain the MRZ and for evaluating consistency accordingly.

Additionally in accordance with at least one embodiment of the presentinvention, incoming electronic documents have VIZ and MRZ zones; thesystem also comprises apparatus for supplying images of an incomingdocument in a plurality of scanned illuminations as well as a photoimage from visible illumination, and each image is supplied immediatelyafter a certain illumination has been scanned and even before allilluminations are completed.

Also provided, in accordance with at least one embodiment of the presentinvention, is a computer program product, comprising a computer usablemedium having a computer readable program code embodied therein, thecomputer readable program code adapted to be executed to implement anyof the methods shown and described herein.

Typically, each template used herein includes metadata definingcommonalities of a type of document, typically of a series thereof, suchas physical, visual or contents characteristics of a series of Peruviandriving licenses, superseded a few years later by a newer series of thesame Peruvian driving licenses. Metadata may include location data suchas the number of mm from the edge of the document to a particular zone,font, colors, watermark patterns, ink parameters, etc.

A particular advantage of certain embodiments of the present invention,such as embodiments involving dynamic evolution of weights, is thatknowledge regarding very indicative information may be integrated intothe system. For example, if there is a discrepancy in the production ofcertain indicia in a document (i.e. some indicia are penned rather thanbeing written in security ink) and if the very same indicia are found tocontain VIZ vs. MRZ differences, as described herein, this combinationof findings may be regarded as highly indicative of a forgery and theweights used may reflect this.

A particular advantage of certain embodiments of the present invention,such as embodiments involving dynamic evolution of thresholds, is thatin E-passport identification, information for active authentication maybe accessed from the E-passport's chip and may be compared to visualinformation. For instance, the correspondence between a photograph inthe chip and the visible photograph may be checked, to ascertain thatthe visual photograph has not been tampered with. More or less weight,or higher or lower thresholds, can be dynamically determined, based onpast results. For instance, it may be desired to highly weight UVpattern information, since this is difficult to forge. Thresholds forparameters which are found to be statistically prone to cause falsealarms, are raised, and so forth.

The term “dynamic” as used herein is intended to include provision of anexternal configuration file which may be used to assign values toweights, thresholds and other dynamic elements of certain embodimentsshown and described herein.

Also provided is a computer program product, comprising a computerusable medium or computer readable storage medium, typically tangible,having a computer readable program code embodied therein, the computerreadable program code adapted to be executed to implement any or all ofthe methods shown and described herein. It is appreciated that any orall of the computational steps shown and described herein may becomputer-implemented. The operations in accordance with the teachingsherein may be performed by a computer specially constructed for thedesired purposes or by a general purpose computer specially configuredfor the desired purpose by a computer program stored in a computerreadable storage medium.

Any suitable processor, display and input means may be used to process,display e.g. on a computer screen or other computer output device,store, and accept information such as information used by or generatedby any of the methods and apparatus shown and described herein; theabove processor, display and input means including computer programs, inaccordance with some or all of the embodiments of the present invention.Any or all functionalities of the invention shown and described hereinmay be performed by a conventional personal computer processor,workstation or other programmable device or computer or electroniccomputing device, either general-purpose or specifically constructed,used for processing; a computer display screen and/or printer and/orspeaker for displaying; machine-readable memory such as optical disks,CDROMs, magnetic-optical discs or other discs; RAMs, ROMs, EPROMs,EEPROMs, magnetic or optical or other cards, for storing, and keyboardor mouse for accepting. The term “process” as used above is intended toinclude any type of computation or manipulation or transformation ofdata represented as physical, e.g. electronic, phenomena which may occuror reside e.g. within registers and/or memories of a computer.

The above devices may communicate via any conventional wired or wirelessdigital communication means, e.g. via a wired or cellular telephonenetwork or a computer network such as the Internet.

The apparatus of the present invention may include, according to certainembodiments of the invention, machine readable memory containing orotherwise storing a program of instructions which, when executed by themachine, implements some or all of the apparatus, methods, features andfunctionalities of the invention shown and described herein.Alternatively or in addition, the apparatus of the present invention mayinclude, according to certain embodiments of the invention, a program asabove which may be written in any conventional programming language, andoptionally a machine for executing the program such as but not limitedto a general purpose computer which may optionally be configured oractivated in accordance with the teachings of the present invention. Anyof the teachings incorporated herein may, wherever suitable, operate onsignals representative of physical objects or substances.

The embodiments referred to above, and other embodiments, are describedin detail in the next section.

Any trademark occurring in the text or drawings is the property of itsowner and occurs herein merely to explain or illustrate one example ofhow an embodiment of the invention may be implemented.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions, utilizing terms such as, “processing”, “computing”,“estimating”, “selecting”, “calculating”, “determining”, “generating”,“generating”, “producing”, “detecting”, “associating”, “obtaining”,“analyzing”, “extracting”, “receiving”, “authenticating”, “binarizing”,“capturing”, and “enhancing” or the like, refer to the action and/orprocesses of a computer or computing system, or processor or similarelectronic computing device, that manipulate and/or transform datarepresented as physical, such as electronic, quantities within thecomputing system's registers and/or memories, into other data similarlyrepresented as physical quantities within the computing system'smemories, registers or other such information storage, transmission ordisplay devices. The term “computer” should be broadly construed tocover any kind of electronic device with data processing capabilities,including, by way of non-limiting example, personal computers, servers,computing system, communication devices, processors (e.g. digital signalprocessor (DSP), microcontrollers, field programmable gate array (FPGA),application specific integrated circuit (ASIC), etc.) and otherelectronic computing devices.

The present invention may be described, merely for clarity, in terms ofterminology specific to particular programming languages, operatingsystems, browsers, system versions, individual products, and the like.It will be appreciated that this terminology is intended to conveygeneral principles of operation clearly and briefly, by way of example,and is not intended to limit the scope of the invention to anyparticular programming language, operating system, browser, systemversion, or individual product. Any suitable input device, such as butnot limited to a sensor, may be used to generate or otherwise provideinformation received by the apparatus and methods shown and describedherein. Any suitable output device or display may be used to display oroutput information generated by the apparatus and methods shown anddescribed herein. Any suitable processor may be employed to compute orgenerate information as described herein e.g. by providing one or moremodules in the processor to perform functionalities described herein.Any suitable computerized data storage e.g. computer memory may be usedto store information received by or generated by the systems shown anddescribed herein. Functionalities shown and described herein may bedivided between a server computer and a plurality of client computers.These or any other computerized components shown and described hereinmay communicate between themselves via a suitable computer network.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain embodiments of the present invention are illustrated in thefollowing drawings:

FIG. 1 is a simplified flowchart illustration of a method forAuthentication of Electronic Documents, constructed and operative inaccordance with certain embodiments of the present invention.

FIGS. 2-24 and 26 illustrate aspects of a system for Authentication ofElectronic Documents, constructed and operative in accordance withcertain embodiments of the present invention.

FIGS. 25, 28 and 32 illustrate aspects of methods for electronicidentification of falsified documents which are useful in implementingthe method of FIG. 1 and/or the systems of FIGS. 2-24, according tocertain embodiments of the present invention.

FIG. 27 illustrates aspects of a British driving license authenticationapplication of the system of FIGS. 2-3, the application beingconstructed and operative in accordance with certain embodiments of thepresent invention.

FIG. 29 is a simplified flowchart illustrating aspects of a VIZ fullpage reading application of the system of FIGS. 2-3, the applicationbeing constructed and operative in accordance with certain embodimentsof the present invention.

FIG. 30 is a diagram of a method for generating an indication of whetheror not a scanned document is authentic, according to certain embodimentsof the present invention.

FIG. 31 is a simplified flowchart illustration of a method forgenerating an indication of whether or not a scanned document isauthentic, according to certain embodiments of the present invention.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

FIG. 1 is a simplified flowchart illustration of a method for scanning,recognizing and processing electronic documents such as traveldocuments. The method typically includes some or all of the followingsteps, suitably ordered e.g. as shown:

Step 110: generate library of scanners, scanning methods (how many scansand in what order, which illuminations etc.) and OCR methods

Step 115: scan incoming documents, using selected scanning method fromlibrary.

Step 120: crop and rotate scanned documents in parallel with step 115

Step 130: binarize cropped, rotated documents

Step 135: OCR binarized documents using selected OCR method from library

Step 140: use templates to identify documents as belonging to knownseries within known document type stored in a document type/seriesdatabase. Typically, each “template” includes data characterizing aseries within a type of document generated by a country, under each ofat least one illumination such as UV or IR. For example, the “template”for series 4 of a French driving license under UV illumination mightinclude a stored indication, in an appropriate database, of some or allof the size, paper type, ink type, coating, printing technology,location of various elements (such as but not limited to photograph,serial number, MRZ area, and issue date), UV illumination-relatedcharacteristics, and other characteristics of the fourth series ofFrench driving licenses. Any suitable method may be employed forbuilding templates given initial example documents of a series.

Step 145: if step 140 fails for a document D, define a new series in thedocument type/series database typified by document D including computingand storing in the document type/series database, metadata for the newseries, and if additional documents arrive which are sufficientlysimilar to document D to belong to the new series, refine the metadatabased on additional documents.

Step 150: if step 140 is omitted, ascertain MRZ is ok given selectedscanning and OCR methods, e.g. by generating checksums. If not, modifyscanning and/or OCR methods.

Step 155: quantify at least one of the following document properties:infra-red text, security paper, UV patterns, 3M laminate, checksum,document issue date not working day, data comparison, e-Passportauthentication (e.g. at least one of: BAC valid or invalid?, data grouphash: valid or invalid?, digital signature: valid or invalid?, signedattributes: valid or invalid?, active authentication: valid orinvalid?), ePassport data comparison, UV dull areas, documentconsistency, Spanish ID print characteristics, and Spanish ID laminateremoval characteristics.

Step 160: dynamically threshold and/or generate a weighted combinationof at least one of the document properties generated in step 155 toobtain an output characterizing each incoming document. In step 160,non-binary data regarding at least one of the typically non-binarydocument properties listed above with reference to step 155, is firstbinarized, using thresholds, to obtain binarized document properties,and is then combined in a weighted combination. Typically, both thethresholds and the weights are not fixed but rather are dynamicallydetermined e.g. by means of one or more external configuration files.

A Software Document Reader (SDR) constructed and operative in accordancewith certain embodiments of the present invention is now described.Typically although not necessarily, the Reader is implemented as asoftware package designed to scan and recognize travel documentation.The SDR works with many different document scanners since its addedvalue is in the recognition, decoding and fraud detection of thedocuments. It is built using an open architecture with a simple uniforminterface. The SDR is typically enhanced, periodically or occasionally,with new document templates and fraud detection techniques.

Typically, the recognition and decoding engine of the SDR uses methodsshown and described herein. In order to reach the best possiblerecognition accuracy, a very large number of documents (such as over amillion) may be scanned and analyzed. The recognition and decodingengine may be trained to recognize hundreds of types of non-standarddocuments (from over 150 countries), including documents without any MRZarea. The recognition accuracy of standard ICAO 9303 travel documents isclose to 100% and over 95% with non-standard documents. The SDR alsocontains a special image optimizer which fixes bad quality images (suchas: from washed-out or worn documents), images scanned at an angle.

Typically, the Fraud Detection module of the SDR is in charge ofrecognizing document frauds. Both document data checks (such as checksumerrors and consistencies between the visible and MRZ areas of thedocument) and image analysis checks are done. The images of a documentare analyzed using methods shown and described herein to determine theirauthenticity based on, inter alia, one or more of checking for B900 ink,security paper, UV patterns, cuts in the retroflective laminate andothers.

There is an optional Image Library component that contains acomprehensive document library. For each document, there are both images(in several illuminations) and information for all document pages. Thiscomponent complements the fraud detection module and allows foradditional manual authentication.

A suitable high-level functionality of the SDR software component, and asuitable breakdown of the inner modules of the SDR, some or all portionsof which may be implemented, are now described with reference to FIG. 2.The SDR supports various scanners as described herein and includes anoptional Image Library component. Recognition accuracy and frauddetection capabilities of the SDR are also described herein.

Typically, the SDR comprises an ActiveX component (OCX) 205 whichtypically interfaces with multiple external scanners using the suppliedmanufacturer's scanner drivers. They may be separately installed on theclient machine where the SDR operates. The SDR may be used in anydevelopment environment that supports ActiveX components (such asMicrosoft Visual C++, Microsoft Visual Basic, Borland Delphi, etc.). TheSDR provides a uniform interface for working with all document scanners.Not all the SDR features are available for every document scanner,depending on the scanner features. For example, some document scannersonly support IR and visible illuminations and therefore are not able toscan UV and 3M illuminations. The SDR software architecture is typicallyopen and modular so as to allow for very quick modifications andenhancements such as new document templates and additional frauddetection techniques.

Typically, the SDR is written as an ActiveX for enhanced usability andease of use. If the SDR is written as an asynchronous component, allmethods result in success or failure events. The SDR supplies images ofthe document in all scanned illuminations and the photo image from thevisible illumination. The images can be received immediately after acertain illumination has been scanned, even before all illuminations arecompleted. The recognition results can also be received before thedocument has been completely scanned. When the document recognition hascompleted, a special event is fired and the host application may accessall the document information. The images can be received in multiplepossible formats: JPEG, TIFF, Bitmap, and PNG. Images can be received intheir original resolution or resized to any requested resolution. JPEGimages can be compressed to sizes of as little as 25 Kb per image. Everydocument field also has a correlating accuracy field. This fieldspecifies any errors or problems found with the specific field. Examplesof such errors include validity errors, rejected characters, checksumerrors and expired dates.

The scanners 210 in FIG. 2 may include some or all of the followingscanners: Oce′ IDS-CSR 4054, RTE 6701, AiT Pax Reader, Regula DocumentReader and others, which vary in their type (full page/swipe/b&w/coloretc.) supported illuminations, resolutions, speeds and interfaces.

Typically, a Scanner Control Engine module 215 controls the differentscanner operations. Each scanner 210 that is added to the SDR isoptimized to work in the best possible way. Even though module 215 workswith many different scanners, it publishes a uniform interface to allowfor maximum modularity and future compatibility. The Scanner ControlEngine 215 updates its operation while scanning a document in accordancewith the document decoder. For example, if a British passport is beingscanned, the UV image may be scanned using an optimized setting (lowerUV gain) and the retroflective image may not be scanned at all, sincethis passport does not have this security feature. The module 215 isable to scan any configuration of illuminations e.g. IR, visible, UV andretroflective. For example, only the IR and UV illuminations or only thevisible illumination, may be scanned. The Scanner Control Engine 215also ensures that all scan operations are done asynchronously in orderto allow the other modules to work in parallel to these operations.

An Image Optimizer module 220 analyzes images received from a scannerand optimizes them for a recognition engine, described in detail below.This operation is especially important for worn or washed out documents.It allows the recognition engine to read these problematic documentswith much better results. In addition, the Image Optimizer locates theimage borders and crops the image accordingly. It also locates thefacial image of the document and extracts its coordinates. The ImageOptimizer is also able to fix images that are scanned at up to 15 degreeangles.

Typically, a Recognition Engine module 225 reads both the visible andMachine Readable Zone (MRZ) of the document, enhances the readability ofthe documents and achieves the best possible recognition accuracy.

Typically, a Document Parser & Decoder module 230 analyzes therecognized document text and parses the data into the output fields,taking into account multiple recognition results obtained from allscanned illuminations. The document parser uses a large documenttemplate database in order to decide what the correct document type isand how to parse the recognized document data. A Fraud Detection module235 is in charge of detecting document frauds. Such fraud detection maycomprise one or both of two types: a. Document data analysis: Typically,encompasses checks based on recognized document data such as checksumerrors, validity errors and consistency checks between the visible andMRZ areas of the document; and b. Image analysis: Typically, encompasseschecks that use image processing techniques to detect frauds. Examplesof some of these checks are: Security Paper Detection & UV PatternAuthentication (UV illumination), B900 ink (IR illumination), anddocument cut detection in 3M laminate (retroflective illumination).

The recognition engine 225 is typically specially tailored forrecognizing travel documentation. Large populations of travel documentsfrom all over the world may be analyzed in pilot testing in order toachieve this. Since a large number of travel documentation does notconform to the ICAO standards, typically many documents from differentnations are analyzed in an ongoing effort, and the SDR of FIG. 2 uses anSDR document database to which new documents are added on an ongoingbasis. The recognition accuracy of the SDR for standard documentsconforming to the ICAO 9303 standard may be close to 100% whereasnon-standard documents, such as the Lichtenstein passport, the Russianvisa or the Canadian permanent resident card may have a lowerrecognition accuracy such as approximately 95%.

Typically, the SDR of FIG. 2 may support many document types, such asbut not limited to non-standard ICAO documents, such as Passports(Standard, Diplomatic, Service, Alien, Emergency, Temporary, etc.),Visas, Identification Cards, Permanent Resident Cards, Border CrossingCards, Reentry Permits, Refugee Travel Documents, Laissez-Passers,Driver Licenses and Immigration Forms. Special learning capabilities maybe added to the SDR in order to cope with the vast amount of traveldocumentation available throughout the world. These capabilities mayinclude the ability to read from both the visible and MRZ areas of thedocument and the ability to read documents without an MRZ area at all.

Typically, US visas are handled as a special case because the expirationdate only appears in the visible area of the document in most of thecases. The visa type is examined and expiry date information isextracted from the correct location. Also, the SDR may recognize all theUS visa subtypes such as Student Visas, Work Visas, etc. In order tomake sure that the recognition accuracy does not suffer when adding newdocument templates, a specialized regression tester may be providedwhich runs a completely automated test, checking hundreds of sampledocuments that are a good representation of most real-world documents.

Fraud Detection module 235, according to certain embodiments of thepresent invention, is now described in detail. Travel documentation isthe main identification measure used to identify a person, hence it isuseful for such documentation to be authenticated. Most biometricidentification systems today rely on the reliable initial identificationof an individual based on proper travel documentation. If the person isable to forge a document at this stage, all future biometric checks areuseless. It is appreciated that if the module correctly identifies somedocument frauds but gives very high false rejects, the fraudidentification process becomes very unreliable and unusable. Therefore,fraud detection techniques are selected in order to minimize the numberof false rejects and may take into account many factors, includingdocument information such as document type and issuing country,particularly in cases where authentic documents are issued that do notconform to the ICAO standards, such as not using B900 ink. The methodsfor detecting document frauds may include:

a. methods which analyze the document data such as checksum errors andconsistency checks between the MRZ and visible areas of the document.The checksum errors can detect changes made to the MRZ. There are alsovalidation checks that ensure, for example, that the issue date occurson a valid working day; and/orb. image analysis checks based on image processing for images of allilluminations, which detect if the document uses B900 ink or is printedon security paper. It can also find cuts (tampering) in theretroflective laminate or if the correct pattern in the UV illuminationis found.

An Image Library Component is optionally provided which includes acomprehensive database of travel documents from countries around theworld. The component is also supplied as an ActiveX component with itsaccompanying database. For every document, the image library can showimages and information about the cover page, data page, flyleaf pagesand others. The images are available in different illuminations such asvisible, IR, UV, retroflective and others. The security features forevery page are highlighted (with red squares) and can be clicked on toshow a magnified image of the area and specific information about thesecurity feature. This library of images and information is useful forcomparing with actual document scans and manually determining if thedocument appears to be authentic. This feature complements the automaticfraud detection engine 235 integrated into the SDR of FIG. 2. The imagelibrary may be updated periodically e.g. quarterly with new documentsand security features.

FIG. 3 is a flowchart of a sample document scanning scenario which maybe performed by the system of FIG. 2. Some or all of the stepsillustrated may be provided, suitably ordered e.g. as shown. Thisexample is based on a scanning scenario where all illuminations arescanned, the document is recognized from the IR illumination and a fraudis detected in the 3M laminate. It is appreciated that in manyapplications, thousands of potential clients need to be identified,qualified and recorded daily. Identity documents may contain 100 or moreidentity data and security features, used for generating authenticationdecisions, in many of which identity data and security features areneither visible nor legible by the human eye, e.g. as shown in FIG. 4.Parameters used may include visual parameters, hidden information and/orinformation which resides on servers.

Customer data entry is often incomplete and involves typographicalerrors, slowly degrading photocopies having to be manually archived,identity fraud occurs and potential clients being rejectedunnecessarily. The Front-End Identity Document Based Authenticationsystems shown and described herein reduce or obviate these problems.FIG. 5 is an example of functionalities which may be performed by aFront-End Identity Document Based Authentication system in accordancewith certain embodiments of the present invention. FIG. 6 illustrateshigh-accuracy Multi-layer document cropping. FIG. 7 illustrateshigh-accuracy Multi-layer document authentication. FIG. 8 is a DocumentID Check opening screen. FIGS. 9-14 illustrate DID Check screen areassuch as system information bar, status bar, document data area, documentimages, test results and action controls, respectively.

Typically, when using a system such as that described hereinabove, adocument is simply placed on a suitable scanner, and a screen displayclearly indicates its authenticity (or not), e.g. “document authentic”as in FIG. 16. IR and UV scans are visible, as shown in the screendisplays of FIGS. 15 and 16 respectively. Other information is shown inFIG. 17. FIGS. 18-20 employ a different example document (a Britishdriving license rather than a Canadian passport). Scans are shown, aswell as verification of DL data (FIG. 20). FIG. 21 employs a differentexample document—a British passport which is authentic but has expired.As shown in FIG. 21, the screen display clearly indicates this. FIGS.22, 23 and 26 are screen displays shown for a fraudulent British drivinglicense. The screen display clearly indicates that the document hasfailed authenticity analysis. FIG. 24 is a screen display for afraudulent Netherlands e-passport.

Example fraud detection methods are now described in detail withreference to FIGS. 25, 28 and 32. Forgery Detection for UV Patterns isnow described. Typically, this includes document forgery detectiontechniques which include checking the travel document's paper responseto the UV radiation, and the existence of security patterns in thedocument printed in UV Fluorescent ink, that can be seen in the visualwavelength range when excited with UV radiation. The methods describedhere are based on checking the visual luminescence of the paper under UVradiation, and on the recognition of patterns visible under UVradiation. The following definitions are employed:

Luminescence: The amount of light (photons) emission from a substancewhose electrons have been excited. Luminescence is cold light, i.e. itis not conditioned by the rise of temperature.

Photoluminescence: Luminescence due to excitation by the adsorption oflight.

Fluorescence and Phosphorescence: Subdivisions of photoluminescence. Thedistinction between them is not always obvious. Fluorescence resultsfrom excited singlet states of electrons, and its typical life time isabout 10 nanoseconds or even shorter. Phosphorescence is the result oftriplet excited states, and its typical lifetime is milliseconds toseconds, and even more.

UV fluorescence: excited by UV irradiation; IR luminescence: excited byvisible light and emitted in the IR.

Fluorescent Probe (fluorophore): Fluorescent substance used to enablefluorescent measurement. Fluorescent probes can be divided intoIntrinsic probes that already exist in the systems to be studied; andExtrinsic probes that are added to the system, and are to be eitherbonded or associated to the studied molecules.

Quenching: The decrease of fluorescence intensity due, for example, tothe interaction with other molecules (quenchers).

Fluorescence spectrum: Data usually presented as emission spectra: Aplot of fluorescence intensity vs. wavelength or wavenumber (reciprocalof wavelength).

Fluorescence is a member of the ubiquitous luminescence family ofprocesses in which susceptible molecules emit light from electronicallyexcited states created by either a physical (for example, absorption oflight), mechanical (friction), or chemical mechanism. Generation ofluminescence through excitation of a molecule by ultraviolet or visiblelight photons is a phenomenon termed photoluminescence, which isformally divided into two categories, fluorescence and phosphorescence,depending upon the electronic configuration of the excited state and theemission pathway. Fluorescence is the property of some atoms andmolecules to absorb light at a particular wavelength and to subsequentlyemit light of longer wavelength after a brief interval, termed thefluorescence lifetime. The process of phosphorescence occurs in a mannersimilar to fluorescence, but with a much longer excited state lifetime.

Fluorescent compounds may be organic (typically aromatic materials),inorganic (ions, doped glasses, and some crystals), and organometallicmaterials. Fluorophores are characterized mostly by their fluorescencelifetime and quantum yield (ratio of number of photons emitted to thenumber absorbed). High intensity of lighting is employed, since theefficiency is usually low. In many analytical studies and uses, anextrinsic fluorophore is added to the system. For the identification offalse documents this is not an option. Fluorescence may be verysensitive to the micro-environment of the emitting molecule. This is oneof the main reasons for the usefulness of fluorescence as an analyticaltool. Fluorescence provides temporal and spatial information. Theintensity of fluorescence may be decreased (quenching) by many competingprocesses in the environment of the fluorophore.

Measurement of fluorescence is depicted as emission spectra. These areplots of fluorescence intensity vs. wavelength (or wavenumber). Twotypes of measurements can be made: steady state and time-resolved. Theformer is the common type of measurement, where the illumination and theobservation are constant. The latter is used to measure decays,following exposure of the sample to a pulse of light. The pulse width istypically shorter than the decay time. The decay may be followed with ahigh-speed system, on the nanosecond time scale. The information gainedis very advantageous; however the equipment is usually very complex andcostly. At least the following two properties of fluorescence are usefulfor false document identification: (a) The same fluorescent emissionspectrum is usually observed, irrespective of the exciting wavelength.There are only rare exceptions to this behavior. This implies that thewavelength of the light sources is less important—emphasis shouldtypically be placed on the detector; and (b) Fluorophores may beselectively excited by polarized light. This opens possibilities forusing polarized light.

A method for UV Security Paper Checking is now described. Underultraviolet light, some papers become fluorescent in the visible range.Papers widely differ in the color of fluorescence. There is alsofluorescence in the IR range, when papers are irradiated in the visiblerange. This has to be detected by photographic or electronic means. Inspecial security papers, small pieces of paper or special fibers may beintroduced into the paper as security markers.

The ICAO (International Civil Aviation Organization) has defined a setof security standards for machine readable travel documents (ICAO Doc9303), including the following concerning UV Security Paper: “Materialsused in the production of travel documents should be of controlledvarieties and obtained only from bona fide security materials suppliers.Materials whose use is restricted to high security applications shouldbe used and materials that are available to the public on the openmarket should be avoided . . . . Security features and/or techniquesshould be included in travel documents to protect against unauthorizedreproduction, alteration and other forms of tampering, including theremoval and substitution of pages in the passport book, especially thebiographical data page. In addition to those features included toprotect blank documents from counterfeiting and forgery, specialattention may be given to protect the biographical data from removal oralteration. A travel document should include adequate security featuresand/or techniques to make evident any attempt to tamper with it.”Moreover, when describing the paper forming the pages of the traveldocument, the ICAO standard indicates in the Basic Features that:“UV-dull paper, or a substrate with a controlled response to UV, suchthat when illuminated by UV light it exhibits a fluorescencedistinguishable in color from the blue used in commonly availablefluorescent materials.”

Various fluorescent materials are used in various travel documents. Thepapers contain zones that are darker, known as UV-dull, and others thatare seen in different wavelength colors. Glues, adhesive tapes,sealants, and (past) application of solvents or chemicals to paper maycause differences in fluorescence, resulting in different fluorescentluminosity and color (wavelength). FIG. 25 is an example of anon-security paper illuminated with UV lighting. Note that all the paperbecomes fluorescent under the UV radiation in FIG. 25, which depicts aforged Security Paper. In order to detect security paper forgery, themethod evaluates a Fluorescent Factor, defined as

${{FF}(R)} = \frac{\sum\limits_{x \in R}{F(x)}}{\sum\limits_{x \in R}{f(x)}}$

Where

R The region in the image to be checked.F(x) The value of the pixel x with a color within the fluorescent range.ƒ(x) The value of a pixel x.

Typically, the factor maximizes when all the pixels in the region havebeen excited by the UV lighting. A document is accepted if the factorfalls within an expected range for the type of document. Note that thevalue is independent of the intensity of the pixels in the region. Inorder to define the analysis regions and expected ranges for differenttypes of documents, a statistical process may be employed to recoverinformation from several thousands of documents in a large number ofdocument types (different travel documents from several countries andauthorized organizations). Also, in order to avoid exogenous factors(like kinds of scanners, quality of the lighting sources, quality of thedocument papers, and others), the same documents may be scanned usingdifferent scenarios. After extended tests, a reliable set of acceptanceparameters was acquired, yielding to very low levels of FRR (FalseRejected Ratio, when rejecting authentic documents) and FAR (FalseAccepted Ratio, when accepting forged documents), with emphasis on FRR,performance and tolerance to scan resolutions (by testing on differentresolutions, from low-quality to high-quality), in order to provide acustomer oriented tool.

Advantages of the method for UV Security Paper Checking described abovemay include one or more of the following: clearly identify securitypaper forgery, very fast, works with low resolutions, easy to set up andoperate, works on a small area of the document image thus less sensitiveto document physical condition, does not require complex pattern imageprocessing and is capable of working with different illuminationintensities.

Typically, a scanner with UV lighting capabilities is used and detectionand acceptance parameters are defined per document type. The method istypically susceptible to background noise in the document image, forexample, due to bad physical condition of the paper or low qualityprint. The method typically does not match the shape of the UV figureand instead only checks for existence of a security paper. Therefore,the above method is particularly suitable for a quick check of securitypapers. Also, when applied to specific regions in the document, it canalso check for the existence of a security pattern, since the patternsuse specific figures with a fixed amount of colored pixels.

Typically, the UV Security Paper Detection method described above checksthe overall response of the document paper to the UV lighting. However,the method above only checks for existence of a security paper, and doesnot verify the existence of UV patterns formed by the use of UVfluorescent ink in the document. In order to verify that the correct UVpattern exists, detailed UV Security Pattern Recognition Methods may beemployed. These are operative to check the colors and shape of thereflected UV figure (in the UV image) against a known UV pattern that isexpected to appear in that specific document type. Since the UV patternis known accurately, and also its location in the document is usuallyknown, a template operator is applied to the document image, and themaximal match is evaluated to check if the UV Security Pattern(template) is found in the image. Possible versions of the templateoperator include the following two versions: (a) Color account, whichevaluates the number of pixels within a color range in a certain area ofthe document; and (b) Shape recognition, which compares the UV image inthe document against an expected security pattern.

The color account method compares the number of pixels within a colorrange, in a certain image region, against the expected number of pixelsfor this region, based on the type of the document. In order to properlyaccount the number of pixels, some or all of the following steps may beperformed, suitably ordered e.g. as shown:

1. The pattern and the image are normalized using the followingNormalized Mean SquaredError operation: Let μ_(a) be the mean intensity of image a. The mean ofthe image is first normalized to 0 by scaling the intensity of eachpixel of a as

$a_{x,y}^{\prime} = {\frac{a_{x,y}}{\mu_{a}} - 1}$

Let s_(a′) be the standard deviation of the new image a′. The intensityof each pixel in a′ is further scaled as

$a_{x,y}^{''} = \frac{a_{x,y}^{\prime}}{s_{a^{\prime}}}$

The resultant image a″ is this of standard deviation 1.2. The pixels in the non-fluorescent range are subtracted from the imageusing Non-Fluorescent Color Subtraction which changes the color of allthe pixels not in the range of the visible fluorescent emission spectrumto black, and so removing all the objects of the image that are not tobe considered in the pattern matching. If v(x) is the image aftersubtracting the non fluorescent pixels from an image ƒ(x), then

${v(x)} = \left\{ \begin{matrix}{{f(x)}\ ,{{{when}\mspace{14mu} {f\ (x)}} \in \left\lbrack {\tau_{0},\tau_{1}} \right\rbrack}} \\{0,{{if}\mspace{14mu} {not}}}\end{matrix} \right.$

where τ₀ and τ₁ define the boundaries of the visible fluorescentemission spectrum and 0 represents a black pixel. These values aredependent on the type of fluorescent ink chosen for the document.Non-fluorescent color subtraction typically comprises a process ofSubtraction of non-Fluorescent pixels.3. After subtraction, a Fluorescent Factor is evaluated for theresulting image, as described above.

Advantages of the UV Security Pattern Recognition Methods describedabove may include some or all of the following: it is fast, it works ondifferent resolutions, it is not affected by the rotation of the image,it does not require a pattern, and it is able to check differences incolor of the fluorescent pictures. These methods typically require UVillumination, do not effectively check the shape of the pattern,typically require external parameters for each type of document, and canbe affected by the light produced with different scanners, since not allUV lenses in scanners have exactly the same characteristics. Therefore,these methods are useful as a fast checking method of UV securitypatterns on documents.

Binary Cross Correlation Factor: The Binary Cross Correlation Factor isa computational operation applied on the image information in order tocheck for existence of UV patterns. It is based on the correlationbetween a function and a pattern. In order to provide a less complexmethod that takes less time to compute and be less sensitive todifferences in scans (different hues, differences between scanners,etc.) the color UV image is transformed into a black & white image.

One standard similarity between a function ƒ(x) and a template t(x) isthe Euclidean distance d(y) squared correlation, given by

${d(y)}^{2} = {\sum\limits_{x}\left\lbrack {{f(x)} - {t\left( {x - y} \right)}} \right\rbrack^{2}}$

where

$\sum\limits_{x}$

means

${\sum\limits_{i = {- M}}^{M}\sum\limits_{j = {- N}}^{N}},$

for some M, N which define the size of the template. If the image atpointy is an exact match, then d(Y)=0; otherwise, d(y)>0. Expanding theexpression for d², the expression is seen as

${d(y)}^{2} = {\sum\limits_{x}\left\lbrack {{f^{2}(x)} - {2{f(x)}{t\left( {x - y} \right)}} + {t^{2}\left( {x - y} \right)}} \right\rbrack}$

Since

$\sum\limits_{x}{t^{2}\left( {x - y} \right)}$

is a constant term it can be neglected. Also,

$\sum\limits_{x}{f^{2}(x)}$

is approximately a constant, and it too can be discounted, leaving whatis called the cross correlation between ƒ and t:

${R_{ft}(y)} = {\sum\limits_{x}{{f(x)}{t\left( {x - y} \right)}}}$

This value is maximized when the portion of the image under ƒ isidentical to t.

If the template t and the image ƒ are binary functions (that is, blackand white pictures), the maximum value of R_(ƒt)(y) is the total numberof pixels in the template t. Moreover, the R_(ƒt)(y) can be furthersimplified by introducing the XOR operator

between ƒ and t, that yields 1 when ƒ(x)=t(x), and so a binary crosscorrelation is given by:

${B_{ft}(y)} = {\sum\limits_{x}{{f(x)} \otimes {t(x)}}}$

The binary cross correlation factor is determined by the ration betweenB_(ƒt)(y) and the size of the template,

${F_{ft}(y)} = \frac{B_{ft}(y)}{S_{t}}$

where S_(t) is the size of the template (number of pixels). The templateis shifted across the image in different offsets (values of y), thesuperimposed values at this offset are “XORed” together, and theproducts are added. The resulting value is entered in a “correlationarray”, whose coordinates are the offset attained by the sourcetemplate. The maximum value in the correlation array indicates theexpected offset of the template in the image. Here, the correlationarray has a maximum of 8 in 1, 2 offset, yielding that this is theposition in the image where the best match was found for the template.Since the objective of the method is to compute the best match for thetemplate, and not establish its position in the image, there is no needto build a correlation array, but only to return the maximum correlationvalue and compare it with a predefined acceptance value (threshold).

Pre-Filtering: The correlation measure employs binarized (black andwhite) images of both the original security pattern and the region inthe document to be checked. Also, because the measure is highly affectedby bright noise (light spots), the shape of the object, its size,orientation, or intensity values, it transforms (filters) the imagebefore applying the pattern recognition method (another option is toapply a normalized correlation, which is less sensitive to the imagecharacteristics than the correlation, although sensitive to thesignal-to-noise content of the images and more costly in computingresources). The following transformations are applied to the image to bechecked:

Non-Fluorescent Color subtraction, to remove the pixels in the image notin the range of visible fluorescent emission spectrum described above,

Edge Detection, to detect the borders of the objects in the image, and

Binarization, to normalize the image to binary values allowing applyingthe binary cross correlation computing.

Edge Detection (step b) is a transformation which detects the boundariesof objects in the image, obtaining a clearer image of the objects to beanalyzed. Edge detection may be effected by approximating the gradientoperation on the image function (i.e. the image data). For an imagefunction ƒ(x), the gradient magnitude s(x) and direction ϕ(x) can becomputed as:

s(x)=(Δ₁ ²+Δ₂ ²)^(1/2)

ϕ(x)=tan⁻¹(Δ₁/Δ₂)

where

Δ₁=ƒ(x+n,y)−ƒ(x,y)

Δ₂=ƒ(x,y+n)−ƒ(x,y)

n is a small integer, usually unity, called the “span” of the gradient.Given a UV image, after subtracting non-fluorescent color pixels, beforeand after applying the Edge Detection transformation, the colors in theresulting image are a representation of the distinct gradient magnitudesin the image.

Binarization (step c) is a transformation which reduces the color depthof an image to a binary level: black and white, by applying abinarization over an image function ƒ(x) computed as:

${b(x)} = \left\{ \begin{matrix}{0,{{{when}\mspace{14mu} {f(x)}} < T}} \\{1,{{if}\mspace{14mu} {not}}}\end{matrix} \right.$

where T is the threshold to be used to differentiate between black andwhite, generally defined as the middle value of the color range. Whenbinarization is applied to a UV image after applying the edge detectiontransformation, it can be seen that the pattern is clearly delineated inthe resulting image.

A typical forgery detection procedure according to certain embodimentsof the present invention is now described. In order to detect forgery bychecking UV security patterns, some or all the following parameters maybe determined according to the type of document. This may be donemanually for each document type and version, since each pattern has itsown specific characteristics including the location of the pattern, mostprominent parts of the pattern, etc. Parameters to be determined mayinclude:

Pattern: the security pattern to be checked. This image may have beenpreviously transformed with non-fluorescent color subtraction, edgedetection and binarization.Check area: the position of the security pattern in the document (top,left, width and height).Fluorescent range: the spectrum range of the visible fluorescent pixels(this value may also be affected by the kind of scanner being used), andThreshold: the acceptance value for binary cross correlation.The detection method typically comprises some or all of the followingsteps, as shown in FIG. 28, suitably ordered e.g. as shown:

Step 2810: Select the check area in the document, enlarging in lowfactor to allow position fluctuations when the document was scanned.

Step 2820: Apply the Non-Fluorescent Color Subtraction on the checkarea, based on the Fluorescent range.

Step 2830: Apply Edge-Detection and Binarization on the check area (bothoperations can be performed in a single pass).

Step 2840: Binary Cross Correlate the check area against the pattern,and compare this value against the Threshold.

The methods described herein are suitable for detecting UV securitypaper and UV security patterns, by verifying the response of the paperto UV radiation, and recognizing patterns in predefined locations in thedocument. It is believed that the use of Fourier metrics can lead toimprovements in the comparison methodologies. Using UV typicallyrequires a special scanner capable of scanning UV illumination, and alsostatistical data recovered from a population of travel documents, inorder to suitably define the parameters employed by the methods.

Forgery Detection based on IR Ink analysis is now described. Such adocument forgery detection technique is operative for checking thatinformation in a travel document has been printed using special securityink (B900), against other printing techniques like Inkjet, Thermal Waxor Laser. The methods described here are based on the specialcharacteristics of security ink, which absorbs light at the infrared 900nm wavelength, compared to other inks or dyeing methods used forprinting, which have various measures of reflectivity. A number ofalternative detection methods are described herein.

B900 ink is an ink which absorbs light in the 900 nm wavelength range(near-infrared). This ink, which is usually made from carbon material,is used as a security feature in passports and other identity or traveldocuments, as a measure against photocopying or digital duplication, asdescribed in ICAO doc9303, Part 1, Section III Paragraph 15.1. Thephysical characteristics of the ink are such that it absorbsnear-infrared light with a wavelength of 900 nm, thus delivering a blackcolor under 900 nm illumination. Security-enabled scanners can scan sucha wavelength. Since regular paper inks and dyes, as well as the paperused in printing processes reflect the near-infrared wavelength, theresult is that information printed using B900 ink appears black, whereasother colors, including the paper itself, reflect light (resulting inwhite or light grey color).

Travel document scanners which support near-infrared illumination scanthe document with special IR-emitting LEDs, resulting in a black & whiteimage where the special ink appears in black and the rest of thedocument appears in white (or light grey). This is done at a wavelengthnot visible to the human eye, using special equipment for scanning.Combined with the use of special character set (OCR-B), and the ISO 1831requirement that any other security features shall not interfere withthe accurate reading of the OCR characters in the B900 range, thisprovides not only a means for detection of forgeries, but also an aidfor more accurate machine reading of the data printed on the document,since the contrast between the black characters and the white backgroundgreatly assists the OCR engine, in addition to filtering-out backgroundgraphics and colors—that appear as an homogeneous “white” background.

IR Scanning Techniques: Special scanners are used to scan lightreflected from a travel document at the 900 nm wavelength. Thesesophisticated scanners employ an array comprising a light sensitivesensor with visible light, IR and UV light sources. A mechanism for fastswitching of light sources and of sensor sensitivity, using mirrors, istypically provided. FIG. 1 of U.S. Pat. No. 7,046,346 describes a lightsource/sensor coupling technique common with IR/UV scanners. The IR LEDlight is reflected through a mirror to illuminate the scanner plate. Thelight reflected from the document is focused using the lens onto thesensor. In order to avoid reflections from the light source on thesensor, the light source emits away from the sensor. In addition, inorder to reduce reflections from the glass plate on the sensor, as wellas to improve readability by the sensor, an optical filter is positionedbetween the lens and the sensor to filter out UV spectrum reflections.

As described, the IR-able scanner delivers a black & white image. Ifsuch is compared to the original color, visible wavelength image to seehow background graphics are dissolved in the IR image, it is apparentthat the paper itself, together with any color graphics and text (exceptfor text printed with B900 ink) returns an almost homogeneousluminosity, whereas the B900 ink does not reflect 900 nm light andappears black.

Spectral Analysis of the IR Image: Typically, in order to detectforgeries, the method analyzes the image information to see whether B900ink was used in the MRZ (and VIZ data) print. As described above, mostpaper and general inks reflect light in the 900 nm wavelength vicinity,and only special inks provide black color in the IR image. This securitymeasure is not visible to the human eye, since the 900 nm wavelength isout of the human eye visible spectrum, and therefore it is considered acoveted security feature.

Typically, cropping may be combined with straightening, as thetechnology itself is similar. Since the scans always show black stripesto the right and to the left of the scanned document, it is possible totake advantage of this knowledge and provide a very fast method forcropping. The method itself horizontally scans the sides of the imagefrom the edge inwards for black pixels, until a bright pixel is reached.Repeating the scan in vertical interleaves can provide the location ofthe left and right edges of the document. By computing the distancebetween the edge of the document and the edge of the image, at each ofthe sampled locations, the vertical tilt of the document can be computedand straightened. Since travel documents are standard, after croppingthe black side stripes it is also possible to compute a suitable crop atthe top of the document, resulting in a final cropped image. Thespectral analysis now appears different. Since the amount of pixelsprinted in B900 ink is relatively small, a small peak at the lowervalues can be seen, and most of the other pixels carry a medium-highluminosity level.

Binarization: Binarization is the transformation of the image from 256shades grayscale into a pure black & white image. This way, only the“real” black pixels remain, thus enhancing precision of forgerydetection methods described below. In a set-up stage, a large number ofscanned IR images of passports may be analyzed, in order to find athreshold that reliably separates the first and the second ranges of IRreflections (black ink and white background). Using this threshold, aquantization method is applied to the image, and the quantization resultis checked against the expected range in order to see whether thedocument is forged. In order to obtain a reliable threshold, the IRimage that is received as a grayscale image is binarized (i.e.,converted into B&W only image). Further enhancements to the imageincrease binarization quality, such as edge-detection. ImageBinarization may use Frei and Chen Edge Operators and Despeckle Methods.When applied to the IR image, the binarization transformation includesSimple Binarization (50% Threshold) followed by Customized Binarization(37% Threshold). The optimized 37% threshold image displays the MRZ andVIZ information much clearer, and even helps by discardingmedium-luminosity textual headings from the VIZ that may obstruct OCRmethods in the VIZ region. Sharpening the image before binarizationyields an even higher-quality binarization, with higher contrast (thusproducing a higher threshold respectively).

Forgery Detection: One aspect of the IR scanning and optimizing is thatthe OCR engine works much better, yielding much better results on the IRimage than the visual light image. Therefore, other methods of forgerydetection such as MRZ checksums and MRZ-VIZ comparison also yield morereliable results. In addition, the binarized IR image can be analyzedfor forgeries. When a non-IR absorbent (B900) ink is used to print aforged document, most of the scanned document image reflects light.

Binarization of the image of the forged document using sharpening+55%threshold results in a white image. When the amount of black pixels inthe binarized image of an authentic passport is counted, the result istypically that 3-6% of the pixels are black. In contrast, the forgeddocument shows less than 1% black pixels. This difference serves as aclear indicator to the lack of B900 ink use in the document printingprocess, thus indicating a forged document. Advantages of this methodinclude high speed, reliability when used in high resolution scanning,un-affected by position or rotation, little influence from the physicalcondition of the document and the fact that no pattern image need beemployed. However, IR inks are readily available, the B900 ink standardis publicly open and known, and the method typically employs a scannerable to scan at the near-infrared wavelength vicinity. The above is asuitable forgery detection method for use when working with IR scannedimages.

In certain special cases, IR forgery detection is limited. For example,the Palestinian Authority travel document is consistently printedwithout using B900 ink. Therefore, if the document is recognized as aPalestinian Authority travel document, IR forgery detection is skipped.Also, some batches or series of French visas are also printed withoutusing B900 ink, therefore yielding a blank image in IR illumination.

Irregular Documents are documents which do not conform to the ICAO 9303standard with regard to the textual information on the document.Usually, these are either non-MRZ documents or national identificationdocuments. One such document is the Israeli national ID card. Suchdocuments present a different spectral spread in the histogram, andtherefore the general threshold used to detect forgeries does not yieldcorrect results.

ICAO Standard 9303 allows for the use of a 2D barcode for presentinginformation in an encrypted form. The barcode is printed in black on arelatively large portion of the document, and its use results in adifferent histogram than expected, thus delivering unreliable results ofthe IR forgery detection method. Since the number of documents known toemploy this feature is not large, it is considered as an acceptablelimitation in the functionality of the method.

The methods described above are operative for detecting forged traveldocuments by verifying that the document contains special B900 securityink which does not reflect light in the 900 nm vicinity. Using IRillumination adds the constraint that a special scanner, capable ofscanning in IR illumination, is used. However, the result is a genericimage analysis method that, when tested on many thousands of passportsand other travel documents, yielded very reliable results.

Document forgery detection techniques operative for checking that thetravel document has been printed using Offset, as opposed to otherprinting techniques like Inkjet, Thermal Wax or Laser, are nowdescribed. The methods described here are based on the smoothnessquality of Offset Printing, compared to the discrete quality of printingbased on the combination of very small basic color dots (dithering), asis done with other printing techniques. A number of alternativedetection methods are shown, indicating their advantages, disadvantagesand utility.

Offset printing is a widely used, sharp, smooth printing technique wherethe inked image is transferred (or “offset”) from a plate first to arubber blanket, then to the printing surface. When used in combinationwith the lithographic process, which is based on the repulsion of oiland water, the offset technique employs a flat (planographic) imagecarrier on which the image to be printed obtains ink from ink rollers,while the non-printing area attracts a film of water, keeping thenonprinting areas ink-free.

Inkjet Printing operates by propelling tiny droplets of liquid ink ontopaper. Inkjet printers are the most common type of computer printer forthe general consumer due to their low cost, high quality of output,capability of printing in vivid color, and ease of use. The dotsproduced by the droplets are very small (usually between 50 and 60microns in diameter), and positioned very precisely, with resolutions ofup to 1440×720 dots per inch (dpi). The dots can have different colorscombined together to create photo-quality images. Although an inkjetprinter can create quality pictures, when magnifying the printed image,the dots producing it can clearly be seen.

Thermal Wax Printing falls somewhere between dye-sublimation and solidink technologies; thermal wax printing uses a wax-coated ribbon andheated pins. As the cyan, magenta, yellow, and black ribbon passes infront of the print head, heated pins melt the wax onto the paper whereit hardens. Thermal wax printers produce vibrant color but may employvery smooth or specially-coated paper or transparencies for best output.Thermal wax printing technology works well for businesses that producelarge quantities of transparencies for colorful business presentations.As with Inkjet Printing, when magnifying an image printed with ThermalWax, the dots producing the image can readily be seen.

Laser printers employ a xerographic printing process, producing theimage by direct scanning of a laser beam across the printer'sphotoreceptor. Compared to Inkjet printers, Laser printers have a higherresolution, no smearing, lower cost per page, and faster print speed.However, laser printers always produce raster images, and except in thehighest-quality versions, are less able to reproduce continuous toneimages such as photographs.

Dithering is a technique used in computer graphics to create theillusion of color depth in images with a limited color palette (colorquantization). In a dithered image, colors not available in the paletteare approximated by a diffusion of colored pixels from within theavailable palette. The human eye perceives the diffusion as a mixture ofthe colors within it. Dithered images, particularly those withrelatively few colors, can often be distinguished by a characteristicgraininess, or speckled appearance. Since non-offset printers usedithering to produce the colors (diffusing the image with very smallcolor dots, in the order of tens of microns) as opposed to Offsetprinting which uses flat colors, checking for dithering is an effectiveway to detect forgery in travel documents. If magnified images in Offsetand Inkjet printing are inspected, Inkjet printing clearly shows thered-green-blue dots used to produce the desired color.

In summary, forgery detection may for example be based on UV securitypaper checking, typically using at least one of UV security patternrecognition, a binary cross correlation factor, and spatial-frequencydomain metric; on IR ink e.g. B900 ink checking, typically usingbinarization; and on offset printing checking, typically using at leastone of dithering checking, pattern dispersion checking and printingcontinuity checking.

A VIZ full page reading application of the system of FIGS. 2-3 is nowdescribed in detail, the application being constructed and operative inaccordance with certain embodiments of the present invention. Themethods shown and described herein enable automatic reading ofinformation from the visual inspection zone (VIZ) part of traveldocuments. The VIZ is not intended for automatic machine reading asopposed to the MRZ—but rather for manual visual inspection. Therefore,many problems arise when trying to process images of the VIZ part oftravel documents with the travel document OCR reading module. Forexample, the international ICAO standard regarding travel documentsallows for greater flexibility regarding information in the VIZ ascompared to the strict rules of MRZ format, such as the precise formatand placement of the information; use of localized information (only alimited alphabet is allowed in the MRZ); etc.

In order to overcome these difficulties, special methods intended forincreasing the efficiency of VIZ readability are now described. Thepurpose of VIZ readability is to enhance security and increase thedetection efficiency of forged travel documents. According to certainembodiments, minimal accuracy is 70% (one mistake in the most importantVIZ information fields) when scanning common travel documents' VIZ forthe following information: Issuing country, Document no., Given name,Surname and Date of birth.

Methods for handling the specific difficulties of VIZ reading may bebased on analysis of the scanning and reading workflow. Operationalexperience in processing various types of travel documents may beutilized to identify and categorize factors and bottlenecks in theprocessing of the image information.

Image processing may include several typically consecutive processes asshown in the simplified flowchart of FIG. 29. The method of FIG. 29typically includes some or all of the following steps 4210-4290,suitably ordered e.g. as shown:

Step 4210: Image capture (from the scanner)

Step 4220: Optimization of the captured image

Step 4230: VIZ section identification and cropping

Step 4240: Binarization for optimizing OCR readability

Step 4250: Definition of fields for OCR operation

Step 4260: Identification of field headings/captions; reading ofheadings/captions

Step 4270: Optimization of OCR according to templates developedspecifically for travel documents previously encountered and analyzedfor template extraction, in a set-up stage and/or as document intake isongoing.

Step 4280: Final information identification.

Step 4290: Error correction and output control

Certain embodiments of the method of FIG. 29 are advantageous vis a visscanning and/or reading with regard to level of quality and/orprocessing time. Example implementations for the steps of FIG. 29 arenow described in detail.

Step 4210—Image Capture: Image capture is performed by a high-qualitytravel document scanner. The scanner is connected to the softwareapplication via the manufacturer's SDK module, which returns an image(in JPG, TIFF or other format). For best readability results, thehighest quality image is employed and therefore a TIFF format file isused.

Step 4220—Image Rotation: Since the final application of this technologyis intended for use by immigration personnel, further consideration maybe taken into account, such as the real-life situations of traveldocument scanning. Since real-life scans are not performed in a lab bytravel document professionals, some mistakes may be made, for example,misplacing of the travel document on the scanner pane. This may providea skewed scanned image, whereas the OCR and the optimization methodsexpect to receive a straight image. In order to overcome thesedifficulties, the captured image may first be rotated.

Rotation of the image is performed by detecting any difference betweenthe black stripe surrounding the document and the luminosity reflectedfrom the travel document. This type of method is called “contrastdetection”. Using this method, it is possible to detect the angle atwhich the document is placed, and using this information each pixel isdisplaced on the document accordingly, to straighten the document.

Step 4240—Binarization: Due to the complexity of their computations, OCRengines work on black and white images only, i.e., a 1 bit plane perpixel. The process of transferring an image from color or grayscale toblack and white only is called “binarization”. Correct binarization isessential to optimize the performance of OCR engines and for the correctreading of the information from the scanned image. A good binarizationoutput retains as much pixel information in the informational part ofthe image (i.e., black) while discarding background images and noise asthe background part of the image (i.e., white). The IR image of thetravel document is used as input for the binarization process, sincethis discards most of the background graphics and colors in the traveldocument. The useful information is usually printed using special IRabsorbing ink, highlighting the desired information. If recognition isnot successful using the IR image, the visible image can also be usedfor recognition. In this case, the binarization process is even morecrucial to the successful recognition of the document.

Vast differences exist between the luminosity values of the scannedimages of passports issued by different countries, and even betweenpassports issued by the same country. Many factors influence the scannedimage, such as light conditions surrounding the scanner, the type of thedocument scanned and its physical condition, etc. By testing manybinarization methods, a single solution may be reached that performsbest as a standard binarization template, i.e., delivers the bestresults in the aggregate. The binarization method analyzes the averageluminosity values of the scanned image and sets an RGB value thatseparates blacks and whites in a manner that best represents the writteninformation in the scanned image.

The large variance in document luminosity may be problematic. Therefore,typically, a clustering method is used in an attempt to find the optimalbinarization setting. The test criterion for stopping the clusteringmethod may be the resulting percentage of black pixels in the resultingB&W image. If the percentage of black pixels in the image is within aspecified predefined range, the method may stop. Otherwise, it wouldraise or lower the color threshold accordingly and test again. A maximumof, say, 7 steps are allowed, to prevent the method from going into anendless loop in some cases. Even using this approach there still may bea few different document types that would benefit from different finalpercentage settings. Therefore, in order to enhance reading quality,several different binarization methods may be used, and the readingengine may choose the method that provides the best results (the fewesterrors from the OCR module).

The output of the binarization method is a black and white only image,where the written information is presented in the best quality that canbe achieved for the specific scan. Binarization may include several“tries”. For example, in a first try the binarization method may producepoor results. The threshold may for example be too low, resulting in thetext being blurred and unclear, which would then lead to very lowreading accuracy. The second try, corrected for this, may produce muchbetter results, which drastically improve the reading accuracy.

OCR Processing—step 4250: After binarization, the B&W image of the VIZis fed into the OCR engine. The OCR method used here is different thanthe one used for MRZ reading, since the character set, as well as thefont used, are not standard (as is the case of the fixed font and sizeof the MRZ). Greater variation in character sets (non-English letterssuch as: Ä, Æ, Ô, Ñ and even non-Latin letters such as Cyrillic,Chinese, etc.) lead to decreased accuracy of the OCR methods, which dealwith more a complex variety of letters. Typically, high-quality scansare employed, as high-resolution images assist in the recognition of thedifferences between the letters.

In addition, where the information fields can be identified and theexpected type of information is fed into the OCR engine (such as names[test], dates [numerals], etc.) the reading quality is much higher, asthe OCR has to cope with fewer variants in the information processed.The OCR engine may be optimized for processing flowing text containingdifferent character sizes, fonts and character sets. A lexicographicdictionary may be used for increased accuracy. A de-speckle filter maybe utilized for removing small artifacts in the resulting image. Postprocessing is performed on the results from the OCR to improve resultsaccording to field type. For example, date fields usually adhere tospecific date templates such as:

DDMMYYYY

MMDDYYYY

DDMMYY

DD-MM-YY

DD/MM/YY

DD MMM YYYY

DD, MMM YYYY

The system attempts to fix the recognized information according to thesetemplates and to the possible character set in these fields. Forexample, DD can only be a value from 1-31, and so on.

Step 4260—Handling Field Headings/Titles: A major obstacle in VIZreading is the separation between the printed personal information ofthe document holder and the field headings/titles. If the information isNOT printed over the headings, then the only problem is separating theactual information from the headings. This is usually straightforward,since most field headings are standard (surname, given name, date ofbirth, date of issue, etc.) If the field headings and the actualinformation overlap, then the recognition method may separate them.Usually, field headings or titles are written in very small fonts,consequently in this case the method may disregard very small letters.The threshold size may be determined by analyzing numerous documents tomeasure the size of their field headings and the actual information.Overlapping leads to greater dependency on the results of the IRscanning and the binarization methods.

Attributing Information to the Correct Field: Since not all traveldocuments adhere to the ICAO standard in the VIZ area, sometimesinformation may appear at unexpected locations in the image. Successfulrecognition of the information is not enough since the correct meaningmay be attributed to the information. If field headings/titles arepresent, they are read and compared to the list of “known headings”, andthen used to identify the various parts of the personal information. Acomplex comparison mechanism is used to compare even partially readheadings.

Step 4270—Use of templates in the event that No Headings Can BeIdentified: In the event that the field headings cannot be identified,the information in the VIZ is divided according to pre-definedtemplates. The templates are based on the ICAO standard together withmodifications for specific countries and document types. The issuingcountry and document type can be derived from the MRZ information tosignal implementation of specialized country-specific templates. Usingsuch pre-defined templates allows the application to “expect” certainvalues at certain areas on the image. However, results may vary. Somecountries use a uniform and mostly consistent template whenmanufacturing a travel document. This is usually the case with more“sophisticated” travel documents, those that employ more security andanti-tampering/forgery features. Documents issued by differentcountries, or other versions of the same passports or other traveldocuments all issued by the same county, may be more prone to variationsin manufacturing, such as absolute positioning of the informationalfields with respect to the fields allocated for them (text that extendsout of bounds).

Step 4280—Processing using knowledge re which Information Can BeExpected in the VIZ. According to the ICAO standard, the followinginformation is included in the VIZ (as well as extra information that isless relevant): Issuing country (*), Type of document, Document number(*), Primary identifier (name) (*), Secondary identifier (name) (*),Date of birth (*), Personal number (not always presented), Gender, Placeof birth, Date of issue of the document and Date of expiration of thedocument (*). The fields marked with an asterisk (*) are typically thosecontaining the most important information for passenger identification.

As described above, in order to provide for VIZ reading, FIG. 29includes several steps to optimize reading quality while maintaining areasonable processing time. In an implementation developed using aC++/Java environment, the total processing time for the scanning of thetravel document and the processing of the information was usually notmore than a mere 2-4 seconds. After the image is captured by thescanner, it is fed into a binarization engine that converts it to ablack and white image optimized for recognition by the OCR. The image issubsequently fed into the OCR engine, using a template “informing” theOCR engine where to look for the information. According to certainembodiments, a computerized system based on the method of FIG. 29 isprovided that is able to recognize VIZ information according to thepredefined criteria.

A British driving license authentication application of the system ofFIGS. 2-3 is now described in detail, the application being constructedand operative in accordance with certain embodiments of the presentinvention. The SDR internal flow is described in the diagram of FIG. 27.Functionalities may include some or all of the following:

-   -   1. RTE Scan & Locate.        -   The locate capability of RTE is not sufficient to conform            with British DL requirements, it cuts the images not            sufficiently accurately.    -   2. Classifier:        -   a. Classifies the two British documents and may reply ‘Un            Known’ to any other document, RB32 is also identified as            RB30, but apparently is in order because it is the same            document.        -   b. Down the road—merge with the Spanish classification to            form a unified classification for the supported docs of            SDR5, optionally may use configuration file in order to be            tuned during the application upon additional documents            accumulated.    -   3. Regular MRZ Document Flow:        -   Upon the above, classifier classifies document as unknown.            The traditional SDR flow may act on that document (assume it            is a passport in this case) including the traditional Crop &            Rotate (which is not used for British DL).    -   4. Parsing Recognizer—        -   a. Uses configuration files that specify every document            attributes, content fields, their coordinates and the other            characteristics.        -   b. Receives document (British DL) type.        -   c. Every field or line is accurately identified and sent to            the TOCR in a very accurate rectangle.        -   d. Returns a vector of fields every field name, value and            confidence level.    -   5. Fraud—UV Pattern        -   a. Comparison of a designated area/s in the UV image for            predefined UV patterns.        -   b. Use configuration file in order to be tuned during the            application upon additional documents accumulated.    -   6. Fraud—DL #2 VIZ        -   a. Check consistency between the names, DOB & DL# fields            according to the DVLA definitions in accordance with            application-specific format and requirements.        -   b. This functionality may be exposed in a separate component            as well to be used in the SDR client outside the SDR.    -   7. SDR XML file & Images        -   SDR may save the customer scan's XML data & images to the            local disk. Later on the files may be used offline for scan            counting and analysis. The files may be saved in folders            structure as directed by the SDR client.    -   8. SDR Configuration files:    -   1. SDR Client—FDI Express: The application uses an SDR Client        called FDI Express which is based on the SDR .NET Client, but        has been developed ever since. The goal is to create a stable,        simple and representative application. It is a main single        screen application with WPF based GUI. Functionality and screen        snapshots are typically as defined by the application. In        addition to this straightforward functionality described in the        above, the SDR Client may stand up to the followingData saving,        as described below.    -   2. Keep a log file—Using Log4Net.    -   3. About screen with Versions and access to the Logs (of SDR and        FDI-X).    -   4. The Left side images (FDI/ISEC Logo and Customer Logo) can be        switched externally without the need to build a new version.    -   5. Display the Station ID and Operator Name. Station ID is        stored in configuration file, Operator Name is inserted by the        operator upon starting the FDI-X.    -   6. SDR Watch dog—a thread that may handle the cases of SDR        getting stuck and restart the SDR.    -   7. Full error handling to avoid customer embarrassment and        enable debugging. FDI-X Configuration Files may include some or        all of the following:        1) Configuration.xml—SDR Configuration        2) ProfileCheck.xml—FDI-X Profile Check specific Configuration        3) Log4NetConfig.xml—log4net Configuration        4) ClientData.xml—contains ScanningCounter, StationIdentifier,        5) ClientToken.xml—which is a private key    -   2. Data Saving Issues: The FDI-X may support data saving for Log        and debriefing purposes. The Data may be stored in XML Files        (and Images as image types according to configuration) the FDI        may use SDR existing saving capabilities and add its own when        desired. The data may be saved in a manner that enables        collecting the data and automatically accumulate it in a        CSV/Excel/DB in order to analyze it later on. Saved Data may        include some or all of the following:    -   1. Customer Check data—Date & Time, Branch ID (Station ID),        Computer name, Staff member name, Check result (Text on the Bar        and its color), Check full results (the various checks results).    -   2. Customer Data—All the data extracted from the document,        explicitly—Names, DOB, Document number, Dates, MRZ when        applicable.    -   3. Images

External Check Data—Type of check (URU\UID\Amberhill WL); Check inputdata (The exact data that was sent), Output/Result. Testingfunctionalities may optionally be provided. Testing document Link TB. Anexternal remote access application may be employed to approach theapplication stations, check logs and images, and update versions, interalia.

FIG. 30 is a diagram of a method for generating an indication of whetheror not a scanned document is authentic, according to certain embodimentsof the present invention. As shown, document characteristics such as butnot limited to resolution, position, material and compressioncharacteristics are determined, measured, or otherwise provided, andthresholding eventually yields the desired binary decision re thedocument being either authentic or non-authentic. Typically, as shown,the method uses information regarding the position of each of thecharacteristics of an individual document to be authenticated, along abell curve describing the distribution of the same characteristic in apopulation of previously scanned and analyzed documents to which theindividual document is thought to belong. Typically, informationregarding a particular document is derived from contour measurements ofthe document rather than from a full image of the document.

According to certain embodiments, a set of predefined tables are storedin a computer memory, each representing a function, typically Gaussian,related to the document type (e.g. passport, ID card, driving license)and/or document origin e.g. country which issued the document. Each ofthe tables is associated with a predefined set of rules, also incomputer memory, which defines weighted results per each possible input,based on accumulated and analyzed information gathered over aninformation gathering time period.

Each scanned document is typically associated with a document type andorigin. The document is measured and checked using various computerizedprocedures, such as but not limited to a resolution measuring process, amaterial checking process, and a compression measuring process. Eachsuch procedure provides a result that serves as input to the system suchas a resolution result, a material-indicating result, and a compressionresult, respectively.

Each of the parameters receives a weighted result based on the input ithas received, respectively. All such results are typically ‘blended’again, using suitable weights, thereby to provide a weighted finalresult. This final result is compared to a pre-defined threshold, inorder to determine whether the document is or is not authenticated.

FIG. 31 is a simplified flowchart illustration of a method forgenerating an indication of whether or not a scanned document isauthentic, according to certain embodiments of the present invention.The method of FIG. 31 typically includes some or all of the followingsteps, suitably ordered e.g. as shown:

Step 4310: receive scanned document

Step 4315: determine country, document type, and series within type towhich document belongs

Step 4317: if response to step 15 is “none”, check: does document belongto unknown type, or to unknown series within known type

Step 4320: if step 15 is successful in finding country-type-series,measure or determine at least one document property, such as resolution,position, material, compression

Step 4330: each document property determined in step 20 is matched tothe normal distribution of that document property—over the population inthe database which matches the document for country, type and series.The deviation from the mean of the distribution and/or the standarddeviation is computed and submitted to a main decision circle.

Step 4340: Main circle results are consolidated and matched anddeviation is computed

Step 4350: If thresholds are passed then authentic, otherwise arenon-authentic.

It is appreciated that terminology such as “mandatory”, “required”,“need” and “must” refer to implementation choices made within thecontext of a particular implementation or application describedherewithin for clarity and are not intended to be limiting since in analternative implantation, the same elements might be defined as notmandatory and not required or might even be eliminated altogether.

It is appreciated that software components of the present inventionincluding programs and data may, if desired, be implemented in ROM (readonly memory) form including CD-ROMs, EPROMs and EEPROMs, or may bestored in any other suitable computer-readable medium such as but notlimited to disks of various kinds, cards of various kinds and RAMs.Components described herein as software may, alternatively, beimplemented wholly or partly in hardware, if desired, using conventionaltechniques. Conversely, components described herein as hardware may,alternatively, be implemented wholly or partly in software, if desired,using conventional techniques.

Included in the scope of the present invention, inter alia, areelectromagnetic signals carrying computer-readable instructions forperforming any or all of the steps of any of the methods shown anddescribed herein, in any suitable order; machine-readable instructionsfor performing any or all of the steps of any of the methods shown anddescribed herein, in any suitable order; program storage devicesreadable by machine, tangibly embodying a program of instructionsexecutable by the machine to perform any or all of the steps of any ofthe methods shown and described herein, in any suitable order; acomputer program product comprising a computer useable medium havingcomputer readable program code, such as executable code, having embodiedtherein, and/or including computer readable program code for performing,any or all of the steps of any of the methods shown and describedherein, in any suitable order; any technical effects brought about byany or all of the steps of any of the methods shown and describedherein, when performed in any suitable order; any suitable apparatus ordevice or combination of such, programmed to perform, alone or incombination, any or all of the steps of any of the methods shown anddescribed herein, in any suitable order; electronic devices eachincluding a processor and a cooperating input device and/or outputdevice and operative to perform in software any steps shown anddescribed herein; information storage devices or physical records, suchas disks or hard drives, causing a computer or other device to beconfigured so as to carry out any or all of the steps of any of themethods shown and described herein, in any suitable order; a programpre-stored e.g. in memory or on an information network such as theInternet, before or after being downloaded, which embodies any or all ofthe steps of any of the methods shown and described herein, in anysuitable order, and the method of uploading or downloading such, and asystem including server/s and/or client/s for using such; and hardwarewhich performs any or all of the steps of any of the methods shown anddescribed herein, in any suitable order, either alone or in conjunctionwith software.

Any computations or other forms of analysis described herein may beperformed by a suitable computerized method. Any step described hereinmay be computer-implemented. The invention shown and described hereinmay include (a) using a computerized method to identify a solution toany of the problems or for any of the objectives described herein, thesolution optionally includes at least one of a decision, an action, aproduct, a service or any other information described herein thatimpacts, in a positive manner, a problem or objectives described herein;and (b) outputting the solution.

Features of the present invention which are described in the context ofseparate embodiments may also be provided in combination in a singleembodiment. Conversely, features of the invention, including methodsteps, which are described for brevity in the context of a singleembodiment or in a certain order may be provided separately or in anysuitable subcombination or in a different order. “e.g.” is used hereinin the sense of a specific example which is not intended to be limiting.Devices, apparatus or systems shown coupled in any of the drawings mayin fact be integrated into a single platform in certain embodiments ormay be coupled via any appropriate wired or wireless coupling such asbut not limited to optical fiber, Ethernet, Wireless LAN, HomePNA, powerline communication, cell phone, PDA, Blackberry GPRS, Satelliteincluding GPS, or other mobile delivery. It is appreciated that in thedescription and drawings shown and described herein, functionalitiesdescribed or illustrated as systems and sub-units thereof can also beprovided as methods and steps therewithin, and functionalities describedor illustrated as methods and steps therewithin can also be provided assystems and sub-units thereof.

1. A document forgery detection device comprising: a processorconfigured for performing at least one fraud detection technique, foreach of plural document types, the device further comprising a databasestoring a plurality of templates each corresponding to an individualseries of an individual type of document in an individual countrythereby to define said plural document types, wherein templates are usedto identify documents as belonging to known series within known documenttype stored in the database, and wherein at least one fraud detectiontechnique used for a given document, is determined as a function of, atleast, which document type the given document belongs to, from amongsaid plural document types.
 2. The device of claim 1, wherein documents'VIZ is scanned to identify the document's issuing country.
 3. The deviceof claim 1, the device having an open software architecture to allow forenhancement including additional fraud detection techniques.
 4. Thedevice of claim 1 wherein the device includes a document parser whichuses the database to decide what the correct document type is; and todecide how to parse data in the document, once said document type hasbeen correctly recognized.
 5. The device of claim 1 wherein eachtemplate used herein includes metadata defining commonalities of a typeof document.
 6. The device of claim 5 wherein said metadata includeslocation of at least one particular zone within the document, for agiven type of document.
 7. The device of claim 1 wherein templates areused to identify documents as belonging to a known series within a knowndocument type stored in said database.
 8. The device of claim 1, thedevice having an open software architecture to allow for enhancementincluding new templates.
 9. A document forgery detection methodcomprising: using a processor for performing at least one frauddetection technique, for each of plural document types, and providing adatabase storing a plurality of templates each corresponding to anindividual series of an individual type of document in an individualcountry thereby to define said plural document types, wherein templatesare used to identify documents as belonging to known series within knowndocument type stored in the database, and wherein at least one frauddetection technique used for a given document, is determined as afunction of, at least, which document type the given document belongsto, from among said plural document types.
 10. A computer programproduct, comprising a non-transitory tangible computer readable mediumhaving computer readable program code embodied therein, said computerreadable program code adapted to be executed to implement a documentforgery detection method comprising: performing at least one frauddetection technique, for each of plural document types, and providing adatabase storing a plurality of templates each corresponding to anindividual series of an individual type of document in an individualcountry thereby to define said plural document types, wherein templatesare used to identify documents as belonging to known series within knowndocument type stored in the database, and wherein at least one frauddetection technique used for a given document, is determined as afunction of, at least, which document type the given document belongsto, from among said plural document types.